> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Llama

[Meta's Llama](https://www.llama.com/) is a family of open-weight autoregressive language models built on the transformer decoder architecture. Key design choices include pre-normalization with RMSNorm, SwiGLU activations, and Rotary Positional Embeddings (RoPE). Llama 3+ models add Grouped Query Attention (GQA) for memory-efficient inference at larger scales.

|                  |                                                 |
| ---------------- | ----------------------------------------------- |
| **Task**         | Text Generation                                 |
| **Architecture** | `LlamaForCausalLM`                              |
| **Parameters**   | 1B – 405B                                       |
| **HF Org**       | [meta-llama](https://huggingface.co/meta-llama) |

## Available Models

* **Llama 3.2**: 1B, 3B
* **Llama 3.1**: 8B, 70B, 405B (128K context)
* **Llama 3**: 8B, 70B
* **Llama 2**: 7B, 13B, 70B
* **LLaMA (v1)**: 7B, 13B, 30B, 65B
* **Yi** (01-ai): 6B, 34B — uses `LlamaForCausalLM`

## Architecture

* `LlamaForCausalLM`

## Example HF Models

| Model          | HF ID                                                                                                       |
| -------------- | ----------------------------------------------------------------------------------------------------------- |
| Llama 3.2 1B   | [`meta-llama/Llama-3.2-1B`](https://huggingface.co/meta-llama/Llama-3.2-1B)                                 |
| Llama 3.2 3B   | [`meta-llama/Llama-3.2-3B`](https://huggingface.co/meta-llama/Llama-3.2-3B)                                 |
| Llama 3.1 8B   | [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)     |
| Llama 3.1 70B  | [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)   |
| Llama 3.1 405B | [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) |
| Llama 3 8B     | [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)         |
| Llama 3 70B    | [`meta-llama/Meta-Llama-3-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)       |
| Llama 2 70B    | [`meta-llama/Llama-2-70b-hf`](https://huggingface.co/meta-llama/Llama-2-70b-hf)                             |
| Yi 34B         | [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B)                                                       |

## Example Recipes

| Recipe                                                                                                                                                        | Description                           |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------- |
| [llama3\_2\_1b\_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml)                         | SFT — Llama 3.2 1B on SQuAD           |
| [llama\_3\_3\_70b\_instruct\_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/llama3_3/llama_3_3_70b_instruct_squad.yaml) | SFT — Llama 3.3 70B Instruct on SQuAD |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml
```

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml
```

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft) for full SFT and LoRA instructions.

## Hugging Face Model Cards

* [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
* [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
* [meta-llama/Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B)
* [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)